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Fu Rong, Meng Lan, Qian Zhang, Lefei Zhang

Referring Remote Sensing Image Segmentation (RRSIS) aims to segment target objects in remote sensing (RS) images based on textual descriptions. Although Segment Anything Model 2 (SAM2) has shown remarkable performance in various segmentation tasks, its application to RRSIS presents several challenges, including understanding the text-described RS scenes and generating effective prompts from text. To address these issues, we propose RS2-SAM2, a novel framework that adapts SAM2 to RRSIS by aligning the adapted RS features and textual features while providing pseudo-mask-based dense prompts. Specifically, we employ a union encoder to jointly encode the visual and textual inputs, generating aligned visual and text embeddings as well as multimodal class tokens. A bidirectional hierarchical fusion module is introduced to adapt SAM2 to RS scenes and align adapted visual features with the visually enhanced text embeddings, improving the model's interpretation of text-described RS scenes. To provide precise target cues for SAM2, we design a mask prompt generator, which takes the visual embeddings and class tokens as input and produces a pseudo-mask as the dense prompt of SAM2. Experimental results on several RRSIS benchmarks demonstrate that RS2-SAM2 achieves state-of-the-art performance.

Siddharth Roheda, Rohit Chowdhury, Aniruddha Bala, Rohan Jaiswal

We propose a novel Auto-Regressive (AR) image generation approach that models images as hierarchical compositions of interpretable visual layers. While AR models have achieved transformative success in language modeling, replicating this success in vision remains challenging due to inherent spatial dependencies in images. Addressing the unique challenges of vision tasks, our method (CART) adds image details iteratively via semantically meaningful decompositions. We demonstrate the flexibility and generality of CART by applying it across three distinct decomposition strategies: (i) Base-Detail Decomposition (Mumford-Shah smoothness), (ii) Intrinsic Decomposition (albedo/shading), and (iii) Specularity Decomposition (diffuse/specular). This “next-detail" strategy outperforms traditional “next-token" and “next-scale" approaches, improving controllability, semantic interpretability, and resolution scalability. Experiments show CART generates visually compelling results while enabling structured image manipulation, opening new directions for controllable generative modeling via physically or perceptually motivated image factorization.

Ji Rao, Xinyu Liu, Yong Yi, Ying Xiao, Ye Luo

Microvascular invasion (MVI) is a critical prognostic factor that significantly impacts postoperative outcomes in hepatocellular carcinoma (HCC). As the current gold standard for the diagnosis of MVI is based on the postoperative histopathological examination of whole slide images, accurate preoperative prediction of MVI status using magnetic resonance imaging (MRI) presents both a substantial clinical imperative and a significant challenge. In order to discover reliable MRI-based imaging biomarkers to support clinical decision making and enhance the interpretability of deep learning-based diagnostic models, we propose a novel interpretable MVI prediction framework in which the shared latent visual attributes are first learned and then used for potential imaging biomarker extraction and MVI diagnosis, respectively. To ensure that the visual attributes of these biomarkers are generalizable across diverse patients, the similarity constraints at the intra-patient level and the inter-patient level are enforced within the learned feature space, enabling intuitive biomarker discovery directly from the original image space. To guarantee semantic alignment between biomarkers and the characteristics of individual patients, we introduce a novel classification mechanism that directly links the alignment between each biomarker and patient-specific characteristics with the prediction, thereby ensuring a precise prediction of MVI. Furthermore, the interpretability of the model is enhanced by integrating a mask-based visual explanation method that highlights regions in patient images that correspond to the identified biomarkers. Extensive experiments on two MVI prediction datasets: HCC-WCH and HCC-ZSH unequivocally demonstrate our method's superior performance in both classification accuracy and interpretability.

Matthew Raffel, Lizhong Chen

The Kolmogorov-Arnold Network (KAN) has been gaining popularity as an alternative to the multilayer perceptron (MLP) due to its greater expressiveness and interpretability. Even so, KAN suffers from training instability and being orders of magnitude slower due to its increased computational cost, limiting its applicability to large-scale tasks. Recently, the Kolmogorov-Arnold Transformer (KAT) has been proposed, achieving FLOPs comparable to traditional Transformer models with MLPs by leveraging Group-Rational KAN (GR-KAN). Unfortunately, despite the comparable FLOPs, our testing shows that KAT remains 123x slower during training, indicating that there are other performance bottlenecks beyond FLOPs. In this paper, we conduct a series of experiments to understand the root cause of the slowdown in KAT. We uncover that the slowdown can be isolated to memory stalls, linked more specifically to inefficient gradient accumulations in the backward pass of GR-KAN. To address this memory bottleneck, we propose FlashKAT, which minimizes accesses to slow memory and the usage of atomic adds through a restructured kernel. Evaluations show that FlashKAT achieves up to an 86.5x training speedup over state-of-the-art KAT while reducing rounding errors in gradient computation.

Karim Radouane, Sylvie Ranwez, Julien Lagarde, Andon Tchechmedjiev

In this paper, we address a challenging task, synchronous motion captioning, that aim to generate a language description synchronized with human motion sequences. This task pertains to numerous applications, such as aligned sign language transcription and unsupervised action segmentation and temporal grounding. Our method introduces mechanisms to control self- and cross-attention distributions of the Transformer, allowing interpretability and aligned text generation. We achieve this through masking strategies and structuring losses that push the model to maximize attention only on the most important frames contributing to the generation of a motion word. These constraints aim to prevent undesired mixing of information in attention maps and to provide a monotonic attention distribution across tokens. Thus, the cross attentions of tokens are used for progressive text generation in synchronization with human motion sequences. We demonstrate the superior performance of our approach through evaluation on the two available benchmark datasets, KIT-ML and HumanML3D. As visual evaluation is essential for this task, we provide a comprehensive set of animated visual illustrations of the output of synchronous text generation in the code repository.

Chenfan Qu, Yiwu Zhong, Jian Liu, Xuekang Zhu, Bohan Yu, Lianwen Jin

The growing prevalence of tampered images poses serious security threats, highlighting the urgent need for reliable detection methods. Multimodal large language models (MLLMs) demonstrate strong potential in analyzing tampered images and generating interpretations. However, they still struggle with identifying micro-level artifacts, exhibit low accuracy in localizing tampered text regions, and heavily rely on expensive annotations for forgery interpretation. To this end, we introduce TextShield-R1, the first reinforcement learning based MLLM solution for tampered text detection and reasoning. Specifically, our approach introduces Forensic Continual pre-training, an easy-to-hard curriculum that well prepares the MLLM for tampered text detection by harnessing the large-scale cheap data from natural image forensic and OCR tasks. During fine-tuning, we perform Group Relative Policy Optimization with novel reward functions to reduce annotation dependency and improve reasoning capabilities. At inference time, we enhance localization accuracy via OCR Rectification, a method that leverages the MLLM’s strong text recognition abilities to refine its predictions. Furthermore, to support rigorous evaluation, we introduce Text Forensics Reasoning (TFR) benchmark, comprising over 45k real and tampered images across 16 languages, 10 tampering techniques, and diverse domains. Rich reasoning-style annotations are included, allowing for comprehensive assessment. Our TFR benchmark simultaneously addresses seven major limitations of existing benchmarks and enables robust evaluation under cross-style, cross-method, and cross-language conditions. Extensive experiments demonstrate that TextShield-R1 significantly advances the state of the art in interpretable tampered text detection.

Bin Pu, Jiewen Yang, Xingguo Lv, Kai Xu, Kenli Li

Echocardiography and vascular ultrasound are essential for comprehensive cardiovascular assessment, yet manual evaluation and writing reports are labor-intensive, time-consuming, and require expertise from both cardiology and vascular surgery departments. Current automated report generation systems mainly focus on X-ray or CT, often neglecting echocardiographic modalities and critical quantitative parameters like aortic diameter and main pulmonary artery diameter, limiting their clinical utility. Moreover, the interdependence between cardiac and peripheral vascular health necessitates cross-departmental insights, which existing methods fail to incorporate. To address these limitations, we first propose the vision-language framework named the Echo-Cardiac-Vascular (ECV), for joint cardiac and vascular ultrasound report generation and parameter measurements. ECV introduces a Mixture-of-Experts vision encoder tailored for distinct ultrasound subtypes, a structured parameter measurement module for accurate quantification, and task-specific decoders that generate interpretable, multimodal diagnostic reports. Our framework, trained on 10K+ paired records, achieves high accuracy, improving diagnostic efficiency, consistency, and cross-disciplinary clinical applicability.

Joao Alexandre Cardeira Pereira, Vasco Lopes, João C. Neves, David Semedo

Video Anomaly Understanding (VAU) is a novel task focused on describing unusual occurrences in videos. Despite growing interest, the evaluation of VAU remains an open challenge. Existing benchmarks rely on n-gram-based metrics (e.g., BLEU, ROUGE-L) or LLM-based evaluation. The first fails to capture the rich, free-form, and visually grounded nature of LVLM responses, while the latter focuses on assessing language quality over factual relevance, often resulting in subjective judgments that are misaligned with human perception. In this work, we address this issue by proposing FineVAU, a new benchmark for VAU that shifts the focus towards rich, fine-grained and domain-specific understanding of anomalous videos. We formulate VAU as a three-fold problem, with the goal of comprehensively understanding key descriptive elements of anomalies in video: events (What), participating entities (Who) and location (Where). Our benchmark introduces a) FV-Score, a novel, human-aligned evaluation metric that assesses the presence of critical visual elements in LVLM answers, providing interpretable, fine-grained feedback; and b) FineW³, a novel, comprehensive dataset curated through a structured and fully automatic procedure that augments existing human annotations with high quality, fine-grained visual information. Human evaluation reveals that our proposed metric has a superior alignment with human perception of anomalies in comparison to current approaches. Detailed experiments on FineVAU unveil critical limitations in LVLM's ability to perceive anomalous events that require spatial and fine-grained temporal understanding, despite strong performance on coarse grain, static information, and events with strong visual cues.

GuangHao Meng, Jinpeng Wang, Jieming Zhu, Letian Zhang, Yong Jiang, Dan Zhao, Qing Li

Vision-language retrieval (VLR), which uses text or image queries to retrieve corresponding cross-modal content, plays a crucial role in multimedia and computer vision tasks. However, challenging concepts in queries often confuse retrievers, limiting their ability to align concepts with visual content. Existing query optimization methods neglect retrievers’ preferences (i.e., text descriptions that better match their corresponding visual content), resulting in unadapted to the retriever and leading to suboptimal performance. To address this, we propose the Retriever-Adaptive Query Optimization (RAQO), an interpretable framework that rewrites queries based on retriever-specific preferences. Specifically, we first leverages multimodal large language Models (MLLMs) and retrieval's feedback to construct the MLLMs-Driven Preference-Aware Dataset Engine (MPADE), which automatically refine queries offline, capturing the retriever’s implicit preferences. Then, we introduce a ``detect-then-rewrite" chain-of-thought rewriting (ReCoT) strategy equipped with a progressive preference alignment pipeline, including three stages: ambiguity detection fine-tuning, query rewriting fine-tuning, and preference rank optimization. This design enables the rewriter to focus on confusing concepts and produce retriever-adapted, high-quality queries. Extensive VLR benchmark experiments have demonstrated the superiority of RAQO in cross-modal retrieval, as well as its interpretability, generalizability and transferability.

Xin Mei, Rui Mao, Xiaoyan Cai, Libin Yang, Erik Cambria

Automated interpretation and reporting of chest X-rays (CXRs) hold significant promise in reducing diagnostic errors and supporting radiologists under heavy clinical workloads. However, existing methods typically rely on global visual features and token-level supervision, limiting their sensitivity to subtle abnormalities and reducing their clinical reliability. To address these challenges, we present Reflective X-ray Network (RefleXNet), which systematically integrates multi-scale visual feature fusion and anatomical relational reasoning with a targeted self-reflective learning strategy. RefleXNet first constructs multi-scale visual representations and captures anatomical context through graph-based relational modeling. Building upon these representations, we introduce a targeted self-reflection strategy that uses clinically guided feedback from generated reports to selectively refine abnormality predictions and their associated region-level visual features. Extensive experiments on MIMIC-CXR demonstrate that RefleXNet consistently outperforms state-of-the-art baselines across clinical factual correctness metrics. Notably, our compact 3B-parameter model surpasses several recent models with over twice the parameter count. Additionally, RefleXNet exhibits strong generalization performance in zero-shot evaluations on IU-Xray compared with leading multimodal language models, highlighting its robustness and clinical effectiveness.

Xiafeng Man, Zhipeng Wei, Jingjing Chen

The widespread deployment of large vision models such as Stable Diffusion raises significant legal and ethical concerns, as these models can memorize and reproduce copyrighted content without authorization. Existing detection approaches often lack robustness and fail to provide rigorous theoretical underpinnings. To address these gaps, we formalize the concept of copyright infringement and its detection from the perspective of Differential Privacy (DP), and introduce the conditional sensitivity metric, a concept analogous to sensitivity in DP, that quantifies the deviation in a diffusion model's output caused by the inclusion or exclusion of a specific training data point. To operationalize this metric, we propose D-Plus-Minus (DPM), a novel post-hoc detection framework that identifies copyright infringement in text-to-image diffusion models. Specifically, DPM simulates inclusion and exclusion processes by fine-tuning models in two opposing directions: learning or unlearning. Besides, to disentangle concept-specific influence from the global parameter shifts induced by fine-tuning, DPM computes confidence scores over orthogonal prompt distributions using statistical metrics. Moreover, to facilitate standardized benchmarking, we also construct the Copyright Infringement Detection Dataset (CIDD), a comprehensive resource for evaluating detection across diverse categories. Our results demonstrate that DPM reliably detects infringement content without requiring access to the original training dataset or text prompts, offering an interpretable and practical solution for safeguarding intellectual property in the era of generative AI.

Yulan Ma, Fangkun Li, Wenchao Yang, Qian Si, Chenglong Yu, Yang Li

Dynamic brain networks provide a powerful representation for capturing temporal variations in functional brain connectivity and have gained increasing attention in brain disease diagnosis. However, most existing methods extract features from isolated time windows, making it difficult to capture the high-order dynamic evolution of brain activity. Moreover, these methods often neglect the functional heterogeneity among brain regions, thereby limiting diagnostic performance. To address these limitations, we propose HyperDiag, a novel temporal-regional Hypergraph learning via topology-enhanced state propagation for brain disease Diagnosis. Specifically, we first design a dual-level hypergraph learning strategy: a temporally-evolving hypergraph message passing strategy to capture dynamic high-order dependencies within and across time windows, and meanwhile, a region-wise functional hypergraph learning strategy to capture regional dependencies. Subsequently, we construct a topology-enhanced selective state-space propagation network to integrate complementary information from both the temporally-evolving and region-wise features. Extensive experiments on four brain disorder datasets (ABIDE-I, ADNI, REST-meta-MDD, and Epilepsy) demonstrate that HyperDiag not only outperforms state-of-the-art methods but also identifies biologically meaningful abnormal connections, offering potential biomarkers for clinical interpretation.

Yingjie Ma, Xun Lin, Yong Xu, Weicheng Xie, Zitong Yu

In recent years, face anti-spoofing (FAS) has made notable progress in multimodal fusion, cross-domain generalization, and interpretability. With the development of large language models and reinforcement learning (RL), strategy-based training paradigms offer new opportunities for jointly modeling multimodality, generalization, and interpretability. However, compared to unimodal reasoning, multimodal reasoning introduces more complex logic, such as accurate feature representation and cross-modal verification, which significantly increases reasoning complexity and labeling difficulty. Due to the lack of high-quality annotations in existing multimodal FAS datasets, directly applying RL strategies is sub-optimal, hindering robust multimodal reasoning. In this paper, we find two key issues of supervised fine-tuning combined with reinforcement learning (SFT+RL) paradigms in multimodal FAS reasoning: 1) limited multimodal reasoning paths not only hinder the full utilization of multimodal information but also constrain the model’s exploration space after SFT, thereby affecting the effectiveness of subsequent RL; and 2) the mismatch between single-task supervision and the diversity of multimodal reasoning paths leads to reasoning confusion, where models may exploit shortcuts by directly mapping input images to answers, bypassing the intended reasoning process. These issues further increase the complexity of multimodal reasoning and hinder the effective application of RL strategies. To address these challenges, we propose the PA-FAS framework with a reasoning path enhancement strategy for high-quality extended reasoning sequences construction based on limited annotated data to enrich the reasoning paths and alleviate exploration constraints. Additionally, we introduce an answer shuffling mechanism during SFT for comprehensive multimodal analysis rather than mining superficial cues, thus encouraging deeper reasoning and avoiding shortcut learning. Our method significantly improves multimodal reasoning accuracy and generalization, and successfully unifies multimodal fusion, cross-domain generalization, and interpretability towards trustworthy multimodal FAS.

Xiyu Luo, Haodong Li, Xinxing Cheng, He Zhao, Yang Hu, Xuan Song, Tianyang Zhang

Image-to-image translation models have achieved notable success in converting images across visual domains and are increasingly used for medical tasks such as predicting post-operative outcomes and modeling disease progression. However, most existing methods primarily aim to match the target distribution and often neglect spatial correspondences between the source and translated images. This limitation can lead to structural inconsistencies and hallucinations, undermining the reliability and interpretability of the predictions. These challenges are accentuated in clinical applications by the stringent requirement for anatomical accuracy. In this work, we present TraceTrans, a novel deformable image translation model designed for post-operative prediction that generates images aligned with the target distribution while explicitly revealing spatial correspondences with the pre-operative input. The framework employs an encoder for feature extraction and dual decoders for predicting spatial deformations and synthesizing the translated image. The predicted deformation field imposes spatial constraints on the generated output, ensuring anatomical consistency with the source. Extensive experiments on medical cosmetology and brain MRI datasets demonstrate that TraceTrans delivers accurate and interpretable post-operative predictions, highlighting its potential for reliable clinical deployment.

Liqin Luo, Guangyao Chen, Xiawu Zheng, Yongxing Dai, Yixiong Zou, Yonghong Tian

Visual grounding, the task of linking textual queries to specific regions within images, plays a pivotal role in vision-language integration. Existing methods typically rely on extensive task-specific annotations and fine-tuning, limiting their ability to generalize effectively to novel or out-of-distribution scenarios. To address these limitations, we introduce GroundingAgent, a novel agentic visual grounding framework that operates without any task-specific fine-tuning. GroundingAgent employs a structured, iterative reasoning mechanism that integrates pretrained open-vocabulary object detectors, multimodal large language models (MLLMs), and large language models (LLMs) to progressively refine candidate regions through joint semantic and spatial analyses. Remarkably, GroundingAgent achieves an average zero-shot grounding accuracy of 65.1% on widely-used benchmarks (RefCOCO, RefCOCO+, RefCOCOg), entirely without fine-tuning. Furthermore, by substituting MLLM-generated captions with the original query texts, the accuracy at the selection stage alone reaches approximately 90%, closely matching supervised performance and underscoring the critical role of LLM reasoning capabilities. GroundingAgent also offers strong interpretability, transparently illustrating each reasoning step, thus providing clear insights into its decision-making process.

Chunyao Lu, Tianyu Zhang, Xinglong Liang, Yuan Gao, Luyi Han, Xin Wang, Nika Rasoolzadeh, Tao Tan, Ritse Mann

Accurate prediction of breast cancer recurrence after treatment is essential for improving long-term outcomes. However, existing models are limited by three key challenges: (1) they typically rely on single-modal data, missing cross-modal interactions; (2) they analyze static snapshots, failing to capture disease progression over time; and (3) they often perform coarse feature fusion, lacking semantic disentanglement and interpretability. To address these issues, we propose LUMIN (Longitudinal Multi-modal Knowledge Decomposition Network), a novel framework that integrates longitudinal mammograms and electronic health records (EHRs) for recurrence prediction. LUMIN leverages a vision-language contrastive pretraining backbone to align multi-modal representations and introduces two knowledge extraction modules: (1) a Cross-Modal Disentangled Knowledge Extractor (CM-DKE) that separates shared, complementary, and modality-specific information across imaging and text; and (2) a Temporal Evolution Disentangled Knowledge Extractor (TE-DKE) that captures time-invariant, time-varying, and time-specific features to model disease dynamics. Experiments on a large-scale dataset of 3,924 patients and 19,684 exams show that LUMIN significantly outperforms state-of-the-art baselines, demonstrating its effectiveness in capturing both multi-modal semantics and temporal heterogeneity for recurrence prediction.

Yunyi Liu, Yingshu Li, Zhanyu Wang, Xinyu Liang, Lingqiao Liu, Lei Wang, Luping Zhou

Automated radiology report generation (R2Gen) has advanced significantly, yet evaluation remains challenging due to the complexity of assessing report quality. Traditional metrics often misalign with human judgments, failing to identify specific deficiencies. To address this, we introduce ReFINE, a framework for training an Evaluation Model using a novel margin-based reward enforcement loss. This approach decomposes report quality into fine-grained sub-scores across user-defined criteria, improving interpretability. Leveraging GPT-4, we generate diverse training data with paired accepted and rejected reports to train our model under a reward-based system. The trained ReFINE Score provides both granular sub-scores and an aggregated quality assessment, enabling criterion-specific evaluation. Experimental results demonstrate ReFINE's superior alignment with human judgments, outperforming traditional metrics in model selection. Its robustness is validated across three expert-annotated datasets—including chest X-rays and multimodal reports covering 9 imaging modalities—and under two distinct scoring systems.

Yuanyuan Liu, Haiyang Mei, Dongyang Zhan, Jiayue Zhao, Dongsheng Zhou, Bo Dong, Xin Yang

3D visual grounding (3DVG) identifies objects in 3D scenes from language descriptions. Existing zero-shot approaches leverage 2D vision–language models (VLMs) by converting 3D spatial information (SI) into forms amenable to VLM processing, typically as composite inputs such as specified-view renderings or video sequences with overlaid object markers. However, this VLM ⊕ SI paradigm yields entangled visual representations that compel the VLM to process entire cluttered cues, making it hard to exploit spatial–semantic relationships effectively. In this work, we propose a new VLM ⊗ SI paradigm that externalizes the 3D SI into a form enabling the VLM to incrementally retrieve only what it needs during reasoning. We instantiate this paradigm with a novel View-on-Graph (VoG) method, which organizes the scene into a multi-modal, multi-layer scene graph and allows the VLM to operate as an active agent that selectively accesses necessary cues as it traverses the scene. This design offers two intrinsic advantages: (i) by structuring 3D context into a spatially and semantically coherent scene graph rather than confounding the VLM with densely entangled visual inputs, it lowers the VLM's reasoning difficulty; and (ii) by actively exploring and reasoning over the scene graph, it naturally produces transparent, step-by-step traces for interpretable 3DVG. Extensive experiments show that VoG achieves state-of-the-art zero-shot performance, establishing structured scene exploration as a promising strategy for advancing zero-shot 3DVG.

Jiaqi Liu, Ronghao Fu, Lang Sun, Haoran Liu, Xiao Yang, Weipeng Zhang, Xu Na, Zhuoran Duan, Bo Yang

The emergence of large vision-language models (VLMs) has significantly enhanced the efficiency and flexibility of geospatial interpretation. However, general-purpose VLMs remain suboptimal for remote sensing (RS) tasks. Existing geospatial VLMs typically adopt a unified modeling strategy and struggle to differentiate between task types and interpretation granularities, limiting their ability to balance local detail perception and global contextual understanding. In this paper, we present SkyMoE, a Mixture-of-Experts (MoE) vision-language model tailored for multimodal, multi-task RS interpretation. SkyMoE employs an adaptive router that generates task- and granularity-aware routing instructions, enabling specialized large language model experts to handle diverse sub-tasks. To further promote expert decoupling and granularity sensitivity, we introduce a context-disentangled augmentation strategy that creates contrastive pairs between local and global features, guiding experts toward level-specific representation learning. We also construct MGRS-Bench, a comprehensive benchmark covering multiple RS interpretation tasks and granularity levels, to evaluate generalization in complex scenarios. Extensive experiments on 21 public datasets demonstrate that SkyMoE achieves state-of-the-art performance across tasks, validating its adaptability, scalability, and superior multi-granularity understanding in remote sensing.

Fengchun Liu, Songhan Jiang, Linghan Cai, Ziyue Wang, Yongbing Zhang

While Vision-Language Models (VLMs) have achieved notable progress in computational pathology (CPath), the gigapixel scale and spatial heterogeneity of Whole Slide Images (WSIs) continue to pose challenges for multimodal understanding. Existing alignment methods struggle to capture fine-grained correspondences between textual descriptions and visual cues across thousands of patches from a slide, compromising their performance on downstream tasks. In this paper, we propose PathFLIP (Pathology Fine-grained Language-Image Pretraining), a novel framework for holistic WSI interpretation. PathFLIP decomposes slide-level captions into region-level sub-captions and generates text-conditioned region embeddings to facilitate precise visual-language grounding. By harnessing Large Language Models (LLMs), PathFLIP can seamlessly follow diverse clinical instructions and adapt to varied diagnostic contexts. Furthermore, it exhibits versatile capabilities across multiple paradigms, efficiently handling slide-level classification and retrieval, fine-grained lesion localization, and instruction following. Extensive experiments demonstrate that PathFLIP outperforms existing large-scale pathological VLMs on four representative benchmarks while requiring significantly less training data, paving the way for fine-grained, instruction-aware WSI interpretation in research and clinical practice.